Understanding risk and having the right strategies in place when an incident occurs is becoming more evident and essential. More and more, organizations are faced with the need to measure and reduce their risks. A successful and effective approach to risk management critically depends on the ability to answer key questions: 
- How large are our risks?
- What are the most likely risk scenarios and how severe would the consequences be?
- What elements in our system or organization are the major contributors to risk?
- How will our risk be affected by changes to the system or organization?
- How confident are we about the answers to the above, and how can we increase our confidence?
- Do you have to analyze multiple or single factors to address the uncertainties when reducing your risk? Do you consider such factors as: systems, processes, organization, people, communication and unforeseen elements as part of your risk assessments?
- Do you need to construct scenarios with many elements or factors during your risk assessment? Do you consider elements that could adversely affect your organization's mission, proposal, production, finances, design, acquisition, schedule, requirements, and management?
- Do these elements or factors pose a threat or potential risk on their own or have an effect on other parts of the organization?

ITEM Quantitative Risk Assessment System (iQRAS) can help identify the risks, find the major contributors, effective ways to reduce the risks, and improve your understanding. The initiating event integration with time lines, event sequences, failure probability characterization, risk ranking, and sensitivity analysis, provides you with a powerful, integrated, risk analysis environment. Other Probabilistic Risk Assessment (PRA) tools cannot match the unique integration of capabilities in iQRAS.

Mineral resource estimation has changed considerably in the past 25 years: geostatistical techniques have become commonplace and continue to evolve; computational horsepower has revolutionized all facets of numerical modeling; mining and processing operations are often larger; and uncertainty quantification is becoming standard practice. Recent books focus on historical methods or details of geostatistical theory. So there is a growing need to collect and synthesize the practice of modern mineral resource estimation into a book for undergraduate students, beginning graduate students, and young geologists and engineers. It is especially fruitful that this book is written by authors with years of relevant experience performing mineral resource estimation and with years of relevant teaching experience. This comprehensive textbook and reference fills this need.

Policy makers, mineral exploration experts, and regional planners decide how public lands, which may contain undiscovered resources, should be used or whether to invest in exploration for minerals on a regular basis. Decisions are also made concerning mineral resource adequacy, national policy, and regional development. This book makes explicit the factors that can affect a mineral-related decision so that decision-makers can clearly see the possible consequences of their decisions.

Geostatistical mapping can be defined as analytical production of maps by using field observations, auxiliary information and a computer program that generates predictions. The purpose of this guide is to assist you in producing quality maps by using fully-operational open source software packages: R+gstat/geoR and SAGA GIS. Materials presented in this book have been used for the five-day advanced training course "GEOSTAT: spatio-temporal data analysis with R+SAGA+Google Earth" that is periodically organized by the author and collaborators. This is an open access publication!

In Applied Geostatistics the authors demonstrate how simple statistical methods can be used to analyse earth science data. In clear language, they explain how various forms of the estimation method called kriging can be employed for specific problems. A case study of a simulated deposit is the focus for the book. This model helps the student develop an understanding of how statistical tools work, serving as a tutorial to guide readers through their first independent geostatistical study.